Synthetic aperture radar (SAR) images provide a wealth of information about structures and activities in an imaged scene. During the production of high-resolution, single-polarization SAR imagery, much more data and/or imagery is generated than available researchers and analysts can examine. Thus, automating the recognition of objects and features in SAR imagery is highly desired, e.g., to augment manual visual analysis. Superpixel segmentation (SPS) algorithms can be utilized to divide an image into small regions of close proximity pixels having similar intensities. Applying these SPS algorithms to optical images can reduce image complexity, enhance statistical characterization, and improve segmentation and categorization of scene objects and features. SPS algorithms typically require high signal-to-noise-ratio (SNR) images with low artifacts for accurate segmentation. SAR imagery, however, tends to include speckle, a product of coherent combining and cancelling of multi-path backscattered radar energy, which can complicate the extraction of superpixel segments and even preclude SPS usage.